{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model: Decision Tree"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import _pickle as pickle\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import precision_score, recall_score, accuracy_score, f1_score, confusion_matrix, classification_report\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading in Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_excel('../top10_features.xlsx')\n",
    "df = df.drop(df.columns[0], axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scaling the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "scaler = StandardScaler()\n",
    "\n",
    "features_df = df.drop([\"Decision\"], 1)\n",
    "\n",
    "scaled_df = pd.DataFrame(scaler.fit_transform(features_df), \n",
    "                               index=features_df.index, \n",
    "                               columns=features_df.columns)\n",
    "\n",
    "df = scaled_df.join(df.Decision)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Splitting the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop([\"Decision\"], 1)\n",
    "y = df.Decision\n",
    "\n",
    "# Train, test, split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Helper Functions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Function for plotting confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_confusion_matrix(y_true, y_pred, labels=[\"Sell\", \"Buy\", \"Hold\"], \n",
    "                          normalize=False, title=None, cmap=plt.cm.coolwarm):\n",
    "\n",
    "    cm = confusion_matrix(y_true, y_pred)\n",
    "    fig, ax = plt.subplots(figsize=(12,6))\n",
    "    im = ax.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    ax.figure.colorbar(im, ax=ax)\n",
    "    # We want to show all ticks...\n",
    "    ax.set(xticks=np.arange(cm.shape[1]),\n",
    "           yticks=np.arange(cm.shape[0]),\n",
    "           # ... and label them with the respective list entries\n",
    "           xticklabels=labels, yticklabels=labels,\n",
    "           title=title,\n",
    "           ylabel='ACTUAL',\n",
    "           xlabel='PREDICTED')\n",
    "    # Rotate the tick labels and set their alignment.\n",
    "    plt.setp(ax.get_xticklabels(), rotation=45, ha=\"right\",\n",
    "             rotation_mode=\"anchor\")\n",
    "    # Loop over data dimensions and create text annotations.\n",
    "    fmt = '.2f' if normalize else 'd'\n",
    "    thresh = cm.max() / 1.5\n",
    "    for i in range(cm.shape[0]):\n",
    "        for j in range(cm.shape[1]):\n",
    "            ax.text(j, i, format(cm[i, j], fmt),\n",
    "                    ha=\"center\", va=\"center\",\n",
    "                    color=\"snow\" if cm[i, j] > thresh else \"orange\",\n",
    "                    size=26)\n",
    "    ax.grid(False)\n",
    "    fig.tight_layout()\n",
    "    return ax"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modeling\n",
    "The preferred evaluation metric used will be __Precision__ for each class.  They will be optimized using the __F1 Score-Macro-Average__ to balance the Precision and Recall.  This is done because we want to not only be correct when predicting but also make a decent amount of predictions for each class.  Classes such as 'Buy' and 'Sell' are more important than 'Hold'."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fitting and Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
       "                       max_features=None, max_leaf_nodes=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=1, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, presort=False,\n",
       "                       random_state=None, splitter='best')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Importing the model\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "# Fitting and training\n",
    "clf = DecisionTreeClassifier()\n",
    "clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Printing out Evaluation Metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "        Sell       0.20      0.33      0.25         6\n",
      "         Buy       0.40      0.25      0.31         8\n",
      "        Hold       0.50      0.33      0.40         3\n",
      "\n",
      "    accuracy                           0.29        17\n",
      "   macro avg       0.37      0.31      0.32        17\n",
      "weighted avg       0.35      0.29      0.30        17\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Classifier predictions\n",
    "pred = clf.predict(X_test)\n",
    "\n",
    "#Printing out results\n",
    "report = classification_report(y_test, pred, target_names=['Sell', 'Buy', 'Hold'])\n",
    "print(report)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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IT9BWsnvUoYpIBigJx1hD0UTue19b1GFIGjVVHkxT5cFsjjoQSatbtsyMOgTpUcZ3zOoVJWEREcl9Rq9mN2eakrCIiOSFOFbC8ftaICIikidUCYuISM4zDLP41Z1KwiIikvsMiGF3tJKwiIjkhTiuE45fRCIiInlClbCIiOSFOM6OVhIWEZHcF5xlGHUUu4hfRCIiInlClbCIiOQFdUeLiIhEJYazo5WERUQk55lZRs4T7qv4fS0QERHJE6qERUQkP6g7WkREJBqamCUiIhIFrRMWERGRVKqERUQkP6g7WkREJBo6T1hERCQKMT1POH5fC0RERPKEKmEREckDhmmdsIiISES0baWIiIi0UyUsIiK5z9C2lSIiItGwWHZHKwmLiEheiOPErPhFJCIikidUCYuISO4zYnmAg5KwiIjkAYvljllKwiIikvOMeO4dHb+IRERE8oQqYRERyX0xPcBBSVhERPKAxXJiVvwiEhERyROqhEVEJD9oxywREZGIxHDHLCVhERHJfaYxYREREUmhSlhERPKDliiJiIhEJIbd0UrCIiKSH2I4Ozp+XwtERETyhCphERHJfWZaoiQiIhKZGHZHKwmLiEh+yNDELDMrAOYCq9z9lO7axq82FxERyW5fA17rTUMlYRERyX3tY8IDffT4MTYWOBm4rTdh5VR3dGLRvyib9v6ow5A0mvd0r75MShZ54fGnow5B0qi+rj7qEHovPWPCI8xsbsrzGe4+I+X5jcCVwNDe3CynkrCIiMggW+/uUzt7wcxOAd5193lm9uHe3ExJWERE8sPgT8w6EjjNzE4CSoFKM7vb3c/r6g0aExYRkTxg4UlKA3x0w92vcvex7j4ROBt4orsEDKqERUQkHxjarENERCQfuPtTwFM9tVMSFhGRnOeAa8csERGRKJiOMhQREYlMDJNw/CISERHJE6qERUQkL2hMWEREJAoWzzHh+EUkIiKSJ1QJi4hIflB3tIiISES0Y5aIiEgULJYTs+L3tUBERCRPqBIWEZHcZ8RydrSSsIiI5AVXEhYREYlCz+cBRyF+XwtERETyhCphERHJC+qOFhERiUoMu6OVhEVEJPdp72gRERFJpUpYRERynqOjDEVERKKj7mgRERFpp0pYRETygqPuaBERkQiY1gmLiIhEJoZJOH4RiYiI5AlVwiIikvtMS5REREQi4RoTFhERiVAMK+H4fS0QERHJE6qERUQkL6g7WkREJBKmzTpERESiEsdKOH4RiYiI5AlVwiIikvuM3JkdbWZfT3cgIiIig8dwEgN+pFt/73hZWqMQERHJQ/3tjo5fTS8iItIFJ7e2rfS0RiEiIjLI4jg7usskbGZb6TzZGlA+aBGJiIgMgqxaJ+zuQzMZiPTOyGOPYOx5p1N96AGU1I6graGJptXvsun5+az9y99Z9/jsqEOUHiSSjYxqfIRRjY9R0zyXitY3KUzW05KoYnPxFFZVnMlbQy4kmSiLOlTpo2ljnuaMvR5kcvUSKoq2sb5xBM+tOYTfLfoUq7aNiTo8iaE+dUebWQVwBnCOu588OCFJZwrKyzjwVz9g9BnH7Xy9rJTi4VVU7j+ZIXtNVBLOAqctH02Rb93leklyPbVNT1Db9AR71t3C7NoH2Fa0VwQRSt853z7kBk6dNHOnq2OGrOHMvf7CCRMf4+rZ1zBnzWERxSdk6ylKZlYMnAScA5wA/An4n0GOS1JYUSEH//lWRhx9CMnWVpbfdh8r732IhqUrsIIEQ/beg9H/dhwlo0ZGHar0QpFvpc1KWFX+b6wqP41NJQezPVFNeety9tg6g0lbf0llyyKmrz2BR8fMpy0xJOqQpQef2feeHQn4H8uO4Y6F57GhcTj7jVjA1z50M7sPWct1R17LZx+dwYqt4yKONn9l1cQsMzsO+DRwPPAkcBdwiLtflKHYJLTnNy8OEnBLC3PP/jrvzvznTq83v7OBDbNeiCg66aslQ7/Iwurv0lxQu9P1LQXVvFRyKw2FE5iy6SoqWt9mz7pf8HrVFRFFKr1RXbqR8/e9G4DZqw7j6mevpn0ByTOrjuTNzXtw54kXUV7UyCVTbuM/Zl8bYbT5y4nnmHB3tfmjwCTgKHc/z90fApKZCUvaFdUMY88rLgbg7V/+bpcELNnnpRE375KAUy0edhnNieEAjGp8JFNhST+dOPExyouaAJjxysV0XMG5pn40D715CgBHj32a6tKNmQ5RYqy7JHwQ8H/A383scTP7HFCQmbCk3dhzTqWgrBRPJll60x1RhyMZ4Fa4Yyy4rG11xNFIT44c8ywAy+vGsmTznp22eXLF0QAUJJIcPvq5jMUmKSwYEx7oI926vKO7v+Tu33L3ScD3gA8CxWb2sJldkvZIpFMjjzsKgLpXXqdpxdod161A34dyWUnbOwC0WGXEkUhPJle/AcDCDe/vss2ijXvTmgz+d7t3zeKMxCW78vA4w4E80q1Xs6PdfTYw28y+ChwHnA3MSHs0sothH9wXgC0vLiBRVsqel3+OMZ8+hfIJu+Pu1C9ZztoH/8HSn/2Glo1bIo5W0qGq+SWGtL4FwIbSQyOORrozomwd5UWNAKzetnuX7VqSxWxoHE5txTomVC7PVHiSBbqbmPWhDpccWO/ujxKMF8sgS5SWUDKyBoC2xiamPfM7hu77XneXAUP32YOh++zB2HNP5fnT/z+2LngjomglXaZs/BYQfGtfOvTzEUcj3akqee+L7+bmYd223dRcTW3FOiqL6wY7LOlCti1R+u9OrtWES5bOdvf5ffkgM2sDXiXIHW3Al9392b7cI98UDXtvacr4iz9FQUkxa+5/jMXX/4Jti9+ipHYE4y84k72u+gJlY0Yx9b6bmHXwmbTVN0YYtQzE5M0/obbpHwC8OfRS6or3jzgi6U5pYdOOn7e3FXfbtjl8vaxQfz+jkonZ0WZWCswCSghy7B/d/Zqu2ne3Y9YxXXzAVOBmYHofY2t09wPDexwP/BA4uo/3yCup474FJcWsf/L/mHfOewdYNa1cy+Lrf07rtnr2/dEVVLxvHOM/90neuunOKMKVAapteJT9N30HgM1F+zO/5oaII5KeWMrOvj39D946+UkyxzO3WUcz8BF332ZmRcAzZvawu/9fZ437HJG7zwUGuntAJbAJwMw+bGZ/bX/BzG4xswvN7KNmdn/K9ePM7M8D/Nys0rqtYafnr3//1k7bvfXze9i+fhMAo079yKDHJelX1TyPw989iwRtNBSM45lRD2nbyizQ2Pre76ikoLnbtsUF28P3lA5qTBItD2wLnxaFjy4PPepzEjaz2u5u2I0yM3vZzBYBtwHf76H9E8D7zax9G6iLgNv78blZq3VrPW1NwV9sb2tjy7x/ddrOW1rZ9MKrAAzdZ1LG4pP0GNKymGlrT6bIt9KUGMmsUY/QWKhdlbJB6jhw6vhwZ6pKNgNQt10z3qOSqdnRZlZgZi8D7wKPu3uX69K6m5h1M7sm2xrgCOBrvYpkZ6nd0YcDd5rZfl01dnc3s7uA88zsduBw4DOdxHkJcAnAyH6fzBhT7tQvWUblfpNp2VRHcntLl01bNgeTPQortcVhNilrXcH0NcdTmlxHi1Xy9KiZbC3eJ+qwpJfWN46koaWM8qJGRg9Z02W7osR2RpStB2BZ3fhMhScdpGnbyhFmNjfl+Qx332m1kLu3AQeaWRVwv5nt5+6dVlHdZa25HZ47sAG4zN3f7UfgqQHOMbMRwEiglZ0r8tS+mtuBh4Am4D53b+3kXjMIl0vtZaU5d87xlhcXULnfZIpqhpEoKSbZvL3TdsU1wTfyli27Hgwg8VTcto7pa4+nom05rVbGM7V/YXNJx0UJEneLN+3Fgbu9wgeGv9Zlm71rFlOYCDYcfH3j5EyFJh24pyUJr3f3qb37PN9sZk8RnLvQaRLurjv6GHe/I+Vxp7v/baAJGMDM9iHYfWsDsAzY18xKzGwY8NGUf4HVwGrgu8BvBvq52WjtX58EwBIJqg85oNM2ieIiqg6eAgSbekj8FSbrmL72JCpbXidJEXN2+wPry/o611HiYPaqIwAYX7mCPYYt7bTNMeOC7WbbkgnmrNHa71xmZiPDChgzKwOOBRZ11b67JDwlzbG1jwm/DPweuMDd29x9BfAH4BXgHuClDu+7B1jh7gvTHE9WePeRWdS/tQKAydd8GTrpTtnjGxftqIRX3/dwRuOTvkskmzjqndOp3v4iToLnRt7B2vKTog5L+unhtz+2Y7LVF6bctsvroyrWcNqkhwD458ppbGqqyWh80s5wEgN+9MJo4EkzewV4gWBM+K9dNe6uO7rczD5IF/Pp3f3F3kST0r7LfRbd/Urgyi5ePgr43758Vi7xllYWXvlfTP39zxh+5EEc8udbWfyjX1L/+lsU7zac8Rd9nD2+GgyVb563gJX3PBhxxNItb+OwdWczsmkWAPNrbmBN+ckUJLd18YYEbYnyzMUnfbapqYY7F5zHFw64jaPGPsu1R1zLHQvOZ2NTDfsOX8jXD7qZ8qImGlrKwgMeJAqZOkXJ3V8h2Oa5V7pLwmMINuzoLGoHBn0tjJnNA+qBywf7s+Lsnb8+yYIrfsy+P/omu50wnd1O2LXbcsvLrzH3U1/BW3cZNpcYKW9dwZiGh3Y8P3Dj5Ry4sev/vOsLJzBzXOddnBIfdy48l92HrObUSTM5dsKTHDvhyZ1eb2gp4+rZ1+gs4YjF8SjD7pLwEnePdNGpux8U5efHyds/v4eNz77I+750HsOnH0xJ7QjaGhrZuuANVt/3MMtv/xPeogQsEg3jR89fybOrD+f0PR9k7+o3KC+qZ33jCJ5fczD3LjqLVdvGRB2kxFCOrenJbXUvv8b8z/971GHIADQUTeS+97VFHYYMklkrpzFr5bSow5AuxLES7m6U+Ydmtm/Hi2b2gZQNNERERLLAwDfqGIwk3l0SPpNgHW9HY4GfpT0SERGRPNNdEt7f3f/Z8WJ4lGG6ly+JiIgMKncb8CPduhsT7u5crqJ0ByIiIjJYMrVEqa+6q4QXm9kuOwiY2YmA1kyIiEhWieOYcHeV8DeAv5rZp4B54bWpBAcpnJL2SERERPJMl5Wwuy8G9gf+CUwEJgBPAZ+lf6coiYiIRCbbKmHcvRm4Pdy+8tPANcBbwJ/SHomIiMigGZyJVQPV3XnCk4GzCZLvBoJDF8zdj8lQbCIiImnhQDKGE7O6q4QXAU8Dp7r7EgAz+0ZGohIREckD3c2O/jiwluBIpv81s4/SxYlKIiIicRfHMeHuJmbd7+5nAfsQTMj6BlBrZr8ws4+lPRIREZHB4vHcrKPHE4rdvd7d73H3Uwi2rHwZ+HbaIxEREckzfTpFyd03Ar8MHyIiIlkjjjtm6ShDERHJA1m2RElERCRXZOPe0SIiIjKIVAmLiEheUHe0iIhIRJJRB9AJJWEREckLcayENSYsIiISEVXCIiKS8wZr28mBUhIWEZG8EMfuaCVhERHJC3GshDUmLCIiEhFVwiIikvsckh51ELtSEhYRkZynbStFRERkJ6qERUQkL2h2tIiISERcY8IiIiJRMJIaExYREZF2qoRFRCTnORoTFhERiYzGhEVERCKidcIiIiKygyphERHJfdq2UkREJBpxnZil7mgREZGIqBIWEZG8oNnRIiIiEYnjjllKwiIikhfiWAlrTFhERCQiqoRFRCTnORbL2dFKwiIikvu0TlhERCQ6GhMWERGRHZSERUQkLzg24EdPzGycmT1pZq+Z2QIz+1p37dUdLSIiOc/J2JhwK3C5u79oZkOBeWb2uLsv7KyxkrCIiOSFTIwJu/saYE3481Yzew0YA+R+Ek7usx+Nv34+6jAkjW78wdNRhyBpdvBxH4o6BEmjJfMrog4h00aY2dyU5zPcfUZnDc1sIvBB4LmubpZTSVhERKQraaqE17v71J4amdkQ4E/A1929rqt2SsIiIpLz3CGZoc06zKyIIAHf4+5/7q6tZkeLiIikiZkZ8CvgNXf/aU/tlYRFRCQvuA/80QtHAucDHzGzl8PHSV01Vne0iIjkhQzNjn4Gen9mopKwiIjkhTjuHa3uaBERkYioEhYRkZznoKMMRUREItH7iVUZpSQsIiJ5QWPCIiIisoMqYRERyaus/uAAABLISURBVHnBmHDUUexKSVhERPJCHJOwuqNFREQiokpYRETyQhwnZikJi4hI7tMSJRERkWg4kExGHcWuNCYsIiISEVXCIiKSF9QdLSIiEhElYRERkQi4x3N2tMaERUREIqJKWERE8oLHsD9aSVhERPJCDHOwkrCIiOQHrRMWERGRHVQJi4hIznNtWykiIhIdLVESERGRHVQJi4hIXlB3tIiISEQ8hv3RSsIiIpLztG2liIiI7ESVsIiI5AWNCYuIiEQkGcP+aCVhERHJeU48K2GNCYuIiERElbCIiOQ+bVspIiISFScZwyys7mgREZGIqBIWEZG84DE8T1hJWEREcl4wOzp+3dFKwiIikvsckjGshDUmLCIiEhFVwiIikhfUHS19lkg2MqrxEUY1PkZN81wqWt+kMFlPS6KKzcVTWFVxJm8NuZBkoizqUKUfpo15mjP2epDJ1UuoKNrG+sYRPLfmEH636FOs2jYm6vCkV5zagsWML5zL+MJ5TCicx+6FCyi07bR4CVdsWBd1gEIwJhzDXSuVhOPutOWjKfKtu1wvSa6ntukJapueYM+6W5hd+wDbivaKIELpH+fbh9zAqZNm7nR1zJA1nLnXXzhh4mNcPfsa5qw5LKL4pLdqEsu5qvrgqMOQnng8zxPWmHDMFflW2qyE5RVnM2fkb5k59g0eGL+ex3Z/kSVDL8UxKlsWMX3tCRQkt0UdrvTSZ/a9Z0cC/seyY/jMw7/i5D8/wLdmXc/qbaMoL2rkuiOvZdzQFRFHKn2xuW135jefypstR0QdimQJVcIxt2ToF1lY/V2aC2p3ur6loJqXSm6loXACUzZdRUXr2+xZ9wter7oiokilt6pLN3L+vncDMHvVYVz97NWAAfDMqiN5c/Me3HniRZQXNXLJlNv4j9nXRhit9KTea7it7l6WtUxlqwd/T08o/wGTip6NODLpKIZDwqqE4+6lETfvkoBTLR52Gc2J4QCManwkU2HJAJw48THKi5oAmPHKxbQn4HZr6kfz0JunAHD02KepLt2Y6RClD5p9KP/afvKOBCzxlUz6gB/ppiSc5dwKd4wFl7Wtjjga6Y0jxwQV0vK6sSzZvGenbZ5ccTQABYkkh49+LmOxieQqd0/LI92UhHNASds7ALRYZcSRSG9Mrn4DgIUb3t9lm0Ub96Y1Gfz13LtmcUbiEpHMUxLOclXNLzGk9S0ANpQeGnE00pMRZesoL2oEYPW23bts15IsZkNjMMwwoXJ5RmITyXWeHPgj3TQxK8tN2fgtABxj6dDPRxyN9KSqZMuOnzc3D+u27abmamor1lFZXDfYYYnkhbw6ytDMtnV4fqGZ3dLDe75nZt/s5PpEM/tXumPMdpM3/4Tapn8A8ObQS6kr3j/iiKQnpYVNO37e3lbcbdvm8PWywsZBjUlE0sfMfm1m7/Y2Z6k7OkvVNjzK/pu+A8Dmov2ZX3NDxBFJbxjvfRP3DrOid227608i0n8Zmpj1G+CE3sYUSXe0mU0Afg2MBNYBF7n78g5tDgrbNADPZDzIGKtqnsfh755FgjYaCsbxzKiHtG1llmhsfe/3VFLQ3G3b4oLt4XtKBzUmkXzgzqAsMdr1c3yWmU3sbfvBrITLzOzl9gdwXcprtwB3uvsU4B7gpk7efzvwVXc/vLsPMbNLzGyumc2t25T7e7QOaVnMtLUnU+RbaUqMZNaoR2gsHBd1WNJLqePAqePDnakq2QxA3XbNehdJB/eBP4AR7TknfFwykJgGsxJudPcD25+Y2YXA1PDp4cCZ4c93Af+V+kYzGwZUufs/U9qc2NmHuPsMYAbApPdPjd+oexqVta5g+prjKU2uo8UqeXrUTLYW7xN1WNIH6xtH0tBSRnlRI6OHrOmyXVFiOyPK1gOwrG58psITkZ6td/epPTfrnbiMCXdMntbJtbxW3LaO6WuPp6JtOa1WxjO1f2FzyYeiDkv6YfGmYHOVDwx/rcs2e9cspjARrId4fePkjMQlkus86QN+pFtUSfhZ4Ozw53PpMObr7puBLWZ2VEqbvFWYrGP62pOobHmdJEXM2e0PrC+bHnVY0k+zVwWb+4+vXMEew5Z22uaYcUEnUFsywZw1Wv8tMlDuTjINj3SLKgl/FbjIzF4Bzge+1kmbi4BbzWwOkLdrNBLJJo5653Sqt7+Ik+C5kXewtvykqMOSAXj47Y/tmGz1hSm37fL6qIo1nDbpIQD+uXIam5pqMhqfSK7KRCVsZvcCc4C9zWylmX2uu/aDNibs7kM6PP8NwdRt3P1t4COdvOd7KT/PAw5Iefl7HdvnPG/jsHVnM7JpFgDza25gTfnJ3RxZmKAtUZ65+KRfNjXVcOeC8/jCAbdx1NhnufaIa7ljwflsbKph3+EL+fpBN1Ne1ERDS1l4wIPEXW3BIkrtvU1VhiWCfdwNZ0Lh8zu1Xdl6AG2UZDQ+yRx3/3Rf2mvHrBgrb13BmIaHdjw/cOPlHLjx8i7b1xdOYOa4zrs3JV7uXHguuw9ZzamTZnLshCc5dsKTO73e0FLG1bOvYcVWzXzPBp8cchl7Fu26krLQtvONqmN3unbdxlfZmJyQqdAkxWCM6Q6UkrBIJIwfPX8lz64+nNP3fJC9q9+gvKie9Y0jeH7Nwdy76CxWbRsTdZAiucMhhjlYSTjOGoomct/72qIOQwbRrJXTmLVyWtRhyADdsmVm1CFIllISFhGRnOeoO1pERCQivd77OaOUhEVEJPdlaO/ovorLjlkiIiJ5R5WwiIjkBXVHi4iIREATs0RERKLi8UzCGhMWERGJiCphERHJA4NzCtJAKQmLiEheiGN3tJKwiIjkPCees6M1JiwiIhIRVcIiIpL7YrpjlpKwiIjkhTiOCas7WkREJCKqhEVEJA/oFCUREZFIuIMnk1GHsQslYRERyQtxnJilMWEREZGIqBIWEZG8oDFhERGRKLjHcomSkrCIiOS8uJ4nrDFhERGRiKgSFhGRvJB0LVESERHJPFd3tIiIiKRQJSwiIjnP0exoERGRyGidsIiISBQckjHcO1pjwiIiIhFRJSwiInlBY8IiIiIRcBzXOmEREZEIaJ2wiIiIpFIlLCIieSGOlbCSsIiI5AHX3tEiIiJRcI0Ji4iISCpVwiIikhc8hjtmKQmLiEjuU3e0iIiIpFIlLCIieUA7ZomIiETCgWQMu6OVhEVEJPd5PCdmaUxYREQkIqqERUQkD7hmR4uIiETFPTngR0/M7AQze93MlpjZt3tqr0pYRERyXwbWCZtZAXArcBywEnjBzB5094VdvUeVsIiISHocAixx96Xuvh34HXB6d29QJSwiIjnP8UzMjh4DrEh5vhI4tLs3mHv8Bqr7y8zWAcuijiMDRgDrow5C0kq/09yTL7/TCe4+MuogemJmjxD8TgaqFGhKeT7D3WeEn/FJ4Hh3vzh8fj5wiLt/paub5VQlnA3/IaSDmc1196lRxyHpo99p7tHvNF7c/YQMfMxKYFzK87HA6u7eoDFhERGR9HgB2MvM3mdmxcDZwIPdvSGnKmEREZGouHurmX0ZeBQoAH7t7gu6e4+ScHaaEXUAknb6neYe/U7zkLvPBGb2tn1OTcwSERHJJhoTFhERiYiSsIiISESUhLOMmVnUMYhI18ysKuoYJHsoCWcRMzsY+IyZlUUdi6SfmWmiZJYzszHAbDP7SNSxSHZQEs4uFcCXgY+bWWnUwUj6mNlk4BdmVhJ1LNI/Zmbuvgq4AbjBzA6LOiaJPyXhLGBm+5vZ+e7+FHA5cDHwKSXi7JcyvFAMJAnWFkqWCRNw+1KTtwm2z51hZkdEF5VkAyXh7LA/8G9mdo67zwK+B3wWJeJcUBn+83WgFrgmwlikn9oTcLhRw/UEp+c8AfyPmU2PMjaJNyXhGGuvktz9t8B9wMlmdl5YEX+PIBF/QmPE2cnMxgJ3mtnn3L2FYKihwswmRBya9JKZ7W1mJ6Zc2gv4D3f/A3AZ8D/A/zOzoyIJUGJPE0FiqkP3Fu5+r5nVAeebGe5+t5ldDdwEtAC/jypW6TszG09w7NlPgW+a2RSCSrgM2AdY1vG/AYkXMysCPg6MCX9VjxD8/s4DnnD3pJk9AXwa+JGZHefujRGGLDGkHbNizsw+D4wnODrrVuAogk3BZ7r7b83sSGClu+fDEY5Zz8wSwDDgRwTnjv43YEAVwXj/scBW4BPuvjaqOKV3zGwU8BlgNMEX4dcItix8zt0vM7OzgP2AW9z9negilbhSEo4ZMyt394bw568CpwHXATcCf3L368MzKz8D3BV2e0nMdaxqwyUsnwSWAve7+5Lw+geALwC/cvf5kQQr3erkdzmSYGhoHMFY8BvAnwiOsNuf4AtVt5v4S/7SmHCMmNlJwA/MbJyZFRD8pT4emAqsJVj2UOzu9wG/BGZHF630hbu7mR1gZjeHz58A7iUYQzzLzPYIry8gOIP0Y5EFK11KTcBmdqqZnQDs7e4/JpgRfRYw3t2PAi4EjlIClu4oCceEmZ0C/BB4yt1XECxXGQs8RdAFfbq7bwc+a2anuftfwzWJElNmNsnMzjSzM8JLLUCNmf2/8H/ms4C/AZcCZ5pZlZlVEHRN9/oUFsk8M/siQQ/VUcD/mtm/u/sNBMuTLjWzj7p7g7tviDJOiT8l4RgIx5UuBy529wfMrDT8tv0bgrGmu929xcwuBL4G6Jt1zIWbb/wFOBK40sw+6+4LCZavDCMYXgCYD7wEPOLum929HjhR1VO8mNmeZjYs7NHYjWAo4Rx3/y5wBMGX4wuB24B/Aa9GF61kE82OjodmgiqpKVz3+20zO5pggs5GgkX/JwIHAh939zejC1V6Ymb7AvcAV7n7Q2Z2HlBpZh9w9wVm9l/A9WY2h6Dq/bq7/yulq3N7hOFLB2ZWDXwJ2G5mP3T3d81sA+Hvyd03mdk3gCPd/TdmdpO7t0UZs2QPTcyKgXA98GUE44AfAP4OPAMsBM4AFgP3Awl3XxdVnNI74ZrQWe6eCJ+/AqwCdgdecvcLw+snA6vc/eWoYpWutX8pCv9+nkBQ8bYB1wI/AI4DDnP3VjP7CnAYwYTJpJaWSW+pEo6B8C/6L4FnCSZj/cXdmwHM7BLgFY0tZQ93f8bMTjazpQSzn//o7teZWTHwqpl9193/093/FnGo0r0CoJWgWHnYzCqBK4F6d7/KzIYCs8IvWYcC56oClr5SJRxj4VKkbwOfUhd09jGzjwKPAsXungyvfQ6ocvf/jjQ46ZaZjQDmAoeE3c+7E+xaNx/YBmxy9x+a2YcIxvjfdve3ootYspUmZsWQmY02s68TbE15gRJwdnL3fxCs814MweQe4Ao0aSf23H098BXgCTPbD7gL+K27f5Fg5vpuZvZjYIm7P6kELP2l7uh42kyw4P/09k0cJDu5+0wzS5pZA/AWwSSsx6KOS3oWTqprAV4BvuPut4YvPQ2UANPCf4r0m7qjRTIg7JqudPf7o45F+sbMjgNuBg519y0p13fsbifSX0rCIhmkQxmyU7hE8EbgcHffGHU8kjvUHS2SQUrA2SmcHV0M/N3MpgaX9LuUgVMlLCLSS2Y2xN23RR2H5A4lYRERkYhoiZKIiEhElIRFREQioiQsIiISESVhERGRiGiJkkgXzKyNYIvJQuA1gi1EGzpcfws43903m9nEsN3rKbf5qbvfaWZvExxNCcHBAH8Gvu/uzeH7/uru+4WfewjwE6AWcIITtV4CPh++f9/wM9qAR4BFwA0EJzW1OwdoCONZBJSGn3+ru98xwD8aEUkTzY4W6YKZbXP3IeHP9wDz3P2nHa7fASx29+s7JtMO93obmOru681sCDADaHH3C1LfZ2a1wPPA2e4+JzxG7+PA0+7+Tsd7hc8vDJ9/ucNn7hSPme1BkPx/5u63p+mPSUQGQN3RIr3zNLBnJ9fnAGP6cqNwnemlwBlmVtPh5S8Bd7j7nLCtu/sf2xPwQLj7UoJzq7860HuJSHooCYv0wMwKgRPpcPqRmRUAHwUeTLk8ycxeTnlM6+ye7l5H0JW9V4eX9gPm9SPMszp8blkX7V4E9unH/UVkEGhMWKRrZWb2cvjz08CvOlyfSJAwH095z5vufmAv729piTLw+066owf7M0VkgFQJi3St0d0PDB9fcfftqdeBCUAxQRdyn5jZUIIkvrjDSwuAgwYQc08+SDBZS0RiQElYpJ/CY+2+CnzTzIp6+75wYtbPgQfcfVOHl28BLjCzQ1Pan2dmowYabzhR6ycEx/KJSAyoO1pkANz9JTObD5xN0GU9KaULG+DX7n5T+POT4WznBHA/8P1O7veOmZ0N/MTMdgOSwCyCWc3dOcvMjkp5/kVgdRjPS7y3ROlmzYwWiQ8tURIREYmIuqNFREQioiQsIiISESVhERGRiCgJi4iIRERJWEREJCJKwiIiIhFREhYREYnI/w8YTReNu3rx7wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(y_test, pred, title=\"Confusion Matrix\")\n",
    "np.set_printoptions(precision=1)\n",
    "# Plot non-normalized confusion matrix\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tuning Model Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Parameters to Tune\n",
    "params = {'criterion': ['gini', 'entropy'],\n",
    "          'max_depth': [None, 2, 3, 4, 5, 6],\n",
    "          'min_samples_split': [2, 5, 10],\n",
    "          'min_samples_leaf': [1,2,3,4,5,6]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 216 candidates, totalling 648 fits\n",
      "[CV] criterion=gini, max_depth=None, min_samples_leaf=1, min_samples_split=2 \n",
      "[CV]  criterion=gini, max_depth=None, min_samples_leaf=1, min_samples_split=2, score=(train=1.000, test=0.391), total=   0.0s\n",
      "[CV] criterion=gini, max_depth=None, min_samples_leaf=1, min_samples_split=2 \n",
      "[CV]  criterion=gini, max_depth=None, min_samples_leaf=1, min_samples_split=2, score=(train=1.000, test=0.513), total=   0.0s\n",
      "[CV] criterion=gini, max_depth=None, min_samples_leaf=1, min_samples_split=2 \n",
      "[CV]  criterion=gini, max_depth=None, min_samples_leaf=1, min_samples_split=2, score=(train=1.000, test=0.306), total=   0.0s\n",
      "[CV] criterion=gini, max_depth=None, min_samples_leaf=1, min_samples_split=5 \n",
      "[CV]  criterion=gini, max_depth=None, min_samples_leaf=1, min_samples_split=5, score=(train=0.908, test=0.321), total=   0.0s\n",
      "[CV] criterion=gini, max_depth=None, min_samples_leaf=1, min_samples_split=5 \n",
      "[CV]  criterion=gini, max_depth=None, min_samples_leaf=1, min_samples_split=5, score=(train=0.854, test=0.478), total=   0.0s\n",
      "[CV] criterion=gini, max_depth=None, min_samples_leaf=1, min_samples_split=5 \n",
      "[CV]  criterion=gini, max_depth=None, min_samples_leaf=1, min_samples_split=5, score=(train=0.895, test=0.342), total=   0.0s\n",
      "[CV] criterion=gini, max_depth=None, min_samples_leaf=1, min_samples_split=10 \n",
      "[CV]  criterion=gini, max_depth=None, min_samples_leaf=1, min_samples_split=10, score=(train=0.829, test=0.391), total=   0.0s\n",
      "[CV] criterion=gini, max_depth=None, min_samples_leaf=1, min_samples_split=10 \n",
      "[CV]  criterion=gini, max_depth=None, min_samples_leaf=1, min_samples_split=10, score=(train=0.650, test=0.567), total=   0.0s\n",
      "[CV] criterion=gini, max_depth=None, min_samples_leaf=1, min_samples_split=10 \n",
      "[CV]  criterion=gini, max_depth=None, min_samples_leaf=1, min_samples_split=10, score=(train=0.789, test=0.325), total=   0.0s\n",
      "[CV] criterion=gini, max_depth=None, min_samples_leaf=2, min_samples_split=2 \n",
      "[CV]  criterion=gini, max_depth=None, min_samples_leaf=2, min_samples_split=2, score=(train=0.865, test=0.448), total=   0.0s\n",
      "[CV] criterion=gini, max_depth=None, min_samples_leaf=2, min_samples_split=2 \n",
      "[CV]  criterion=gini, max_depth=None, min_samples_leaf=2, min_samples_split=2, score=(train=0.883, test=0.309), total=   0.0s\n",
      "[CV] criterion=gini, max_depth=None, min_samples_leaf=2, min_samples_split=2 \n",
      "[CV]  criterion=gini, max_depth=None, min_samples_leaf=2, min_samples_split=2, score=(train=0.893, test=0.307), total=   0.0s"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:    0.0s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[CV] criterion=gini, max_depth=None, min_samples_leaf=2, min_samples_split=5 \n",
      "[CV]  criterion=gini, max_depth=None, min_samples_leaf=2, min_samples_split=5, score=(train=0.878, test=0.481), total=   0.0s\n",
      "[CV] criterion=gini, max_depth=None, min_samples_leaf=2, min_samples_split=5 \n",
      "[CV]  criterion=gini, max_depth=None, min_samples_leaf=2, min_samples_split=5, score=(train=0.756, test=0.390), total=   0.0s\n",
      "[CV] criterion=gini, max_depth=None, min_samples_leaf=2, min_samples_split=5 \n",
      "[CV]  criterion=gini, max_depth=None, min_samples_leaf=2, min_samples_split=5, score=(train=0.877, test=0.365), total=   0.0s\n",
      "[CV] criterion=gini, max_depth=None, min_samples_leaf=2, min_samples_split=10 \n",
      "[CV]  criterion=gini, max_depth=None, min_samples_leaf=2, min_samples_split=10, score=(train=0.778, test=0.333), total=   0.0s\n",
      "[CV] criterion=gini, max_depth=None, min_samples_leaf=2, min_samples_split=10 \n",
      "[CV]  criterion=gini, max_depth=None, min_samples_leaf=2, min_samples_split=10, score=(train=0.560, test=0.390), total=   0.0s\n",
      "[CV] criterion=gini, max_depth=None, min_samples_leaf=2, min_samples_split=10 \n",
      "[CV]  criterion=gini, max_depth=None, min_samples_leaf=2, min_samples_split=10, score=(train=0.789, test=0.325), total=   0.0s\n",
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      "  'precision', 'predicted', average, warn_for)\n",
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      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
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      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
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      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
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      "  'precision', 'predicted', average, warn_for)\n",
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      "  'precision', 'predicted', average, warn_for)\n",
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      "  'precision', 'predicted', average, warn_for)\n",
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      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
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      "  'precision', 'predicted', average, warn_for)\n",
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      "  'precision', 'predicted', average, warn_for)\n",
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      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
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      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
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      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
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      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
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      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
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      "  'precision', 'predicted', average, warn_for)\n",
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      "  'precision', 'predicted', average, warn_for)\n",
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    },
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      "  'precision', 'predicted', average, warn_for)\n",
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      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
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    },
    {
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     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] criterion=entropy, max_depth=6, min_samples_leaf=6, min_samples_split=2 \n",
      "[CV]  criterion=entropy, max_depth=6, min_samples_leaf=6, min_samples_split=2, score=(train=0.498, test=0.411), total=   0.0s\n",
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      "[CV] criterion=entropy, max_depth=6, min_samples_leaf=6, min_samples_split=10 \n",
      "[CV]  criterion=entropy, max_depth=6, min_samples_leaf=6, min_samples_split=10, score=(train=0.498, test=0.411), total=   0.0s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "[Parallel(n_jobs=1)]: Done 648 out of 648 | elapsed:    9.5s finished\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py:813: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
      "  DeprecationWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=3, error_score='raise-deprecating',\n",
       "             estimator=DecisionTreeClassifier(class_weight=None,\n",
       "                                              criterion='gini', max_depth=None,\n",
       "                                              max_features=None,\n",
       "                                              max_leaf_nodes=None,\n",
       "                                              min_impurity_decrease=0.0,\n",
       "                                              min_impurity_split=None,\n",
       "                                              min_samples_leaf=1,\n",
       "                                              min_samples_split=2,\n",
       "                                              min_weight_fraction_leaf=0.0,\n",
       "                                              presort=False, random_state=None,\n",
       "                                              splitter='best'),\n",
       "             iid='warn', n_jobs=None,\n",
       "             param_grid={'criterion': ['gini', 'entropy'],\n",
       "                         'max_depth': [None, 2, 3, 4, 5, 6],\n",
       "                         'min_samples_leaf': [1, 2, 3, 4, 5, 6],\n",
       "                         'min_samples_split': [2, 5, 10]},\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\n",
       "             scoring='f1_macro', verbose=5)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "search = GridSearchCV(clf, params, cv=3, return_train_score=True, verbose=5, scoring='f1_macro')\n",
    "\n",
    "search.fit(X,y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tuned Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Training Score: 0.6971390190613382\n",
      "Mean Testing Score: 0.7596683250414594\n",
      "\n",
      "Best Parameter Found:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'criterion': 'gini',\n",
       " 'max_depth': 4,\n",
       " 'min_samples_leaf': 1,\n",
       " 'min_samples_split': 5}"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Mean Training Score:\", np.mean(search.cv_results_['mean_train_score']))\n",
    "print(\"Mean Testing Score:\", search.score(X, y))\n",
    "print(\"\\nBest Parameter Found:\")\n",
    "search.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model with the Best Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=4,\n",
       "                       max_features=None, max_leaf_nodes=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=1, min_samples_split=5,\n",
       "                       min_weight_fraction_leaf=0.0, presort=False,\n",
       "                       random_state=None, splitter='best')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "search_clf = search.best_estimator_\n",
    "\n",
    "search_clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Results from Optimum Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "        Sell       0.29      0.67      0.40         6\n",
      "         Buy       0.00      0.00      0.00         8\n",
      "        Hold       0.50      0.33      0.40         3\n",
      "\n",
      "    accuracy                           0.29        17\n",
      "   macro avg       0.26      0.33      0.27        17\n",
      "weighted avg       0.19      0.29      0.21        17\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Classifier predictions\n",
    "s_pred = search_clf.predict(X_test)\n",
    "\n",
    "#Printing out results\n",
    "report = classification_report(y_test, s_pred, target_names=['Sell', 'Buy', 'Hold'])\n",
    "print(report)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Confusion Matrix for Optimum Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(y_test, s_pred, title=\"Confusion Matrix\")\n",
    "np.set_printoptions(precision=1)\n",
    "# Plot non-normalized confusion matrix\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
